Video surveillance system

BACKGROUND

The invention relates to video surveillance systems and, more particularly, to a method and apparatus for tracking motion in a three dimensional space in video surveillance systems.

In an era of increasing fear of terrorism and corporate scandal, video surveillance has become an increasingly essential part of security. While surveillance cameras can be useful in recording wrongdoings, their greater value is realized when they prevent such acts.

Generally speaking, fixed cameras provide video surveillance for a spatially limited area. As the size of the area increases, the number of cameras that are needed for a given level of surveillance also increases. For instance, using fixed cameras to survey the border between two nations might require thousands of cameras. The same could be true for protecting a large corporation. While most office buildings do not require thousands of cameras for adequate surveillance, tens or hundreds may be needed.

A source of problems with the prior art is that security personnel are required to monitor these cameras either in real time or during replay. There are limits on how many cameras an individual can watch at an acceptable level of efficiency. For instance, studies have shown that an average person can only watch four to five cameras at an acceptable level of efficiency.

Most surveillance really only concerns moving objects. The signal processing of video images generally and, more specifically, for identifying moving objects, is not new. For example, U.S. Pat. No. 5,930,379 discloses modeling an object as a branched kinematic chain composed of links connected at joints. Groups of pixels having like motion parameters are assigned to the links. Motion parameters are estimated until the groups of pixels and their motion parameters converge and can be identified with the moving object.

U.S. Pat. No. 5,987,154 discloses detecting a moving object, calculating the local extremes of curvature of the boundaries of the moving object, comparing the local extremes with a stored model of a human head in order to find regions shaped like a human head, and identifying the head with a surrounding shape.

U.S. Pat. No. 6,049,619 discloses a stratified moving object detection technique which gradually increases in complexity as scene complexity increases (from least complex to most complex): (i) scenarios in which the camera induced motion can be modeled by a single two-dimensional parametric transformation, (ii) those in which the camera induced motion can be modeled in terms of a small number of layers of parametric transformations, and (iii) general three-dimensional scenes, in which a more complete parallax motion analysis is required.

U.S. Pat. No. 6,081,606 discloses processing a sequence of images and generating a flow field representing the motion within a scene. The flow field is a vector representation of the motion of the scene that represents both the magnitude and the direction of the motion. The flow field is generated by correlating at least two frames in the sequence of images. This flow field is analyzed by a flow field segmentor to determine the magnitude and direction of motion within the scene and segment the motion information from the static portions of the scene. An alarm detector then processes the motion information to determine if an alarm should be generated based upon the motion information.

U.S. Pat. Nos. 6,188,777 and 6,445,810 disclose marking and tracking regions of homogenous color. In one approach, each image received at a primary camera is initially represented with pixels corresponding to the red, green, and blue channels of the image, and is converted into a “log color-opponent” space. This space can represent the approximate hue of skin color, as well as its log intensity value. More specifically, (R, G, B) tuples are converted into tuples of the form (l(G), l(R)−l(G), l(B)−(l(R)+l(G))/2), where l(x) indicates a logarithm function. In another approach, a lookup table is precomputed for all input values, quantizing the classification score (skin similarity value) into 8 bits and the input color channel values to 6, 7, or 8 bits.

U.S. Pat. No. 6,504,951 discloses classifying potential sky pixels in the image by color, extracting connected components of the potential sky pixels, eliminating ones of the connected components that have a texture above a predetermined texture threshold, computing desaturation gradients of the connected components, and comparing the desaturation gradients of the connected components with a predetermined desaturation gradient for sky to identify true sky regions in the image.

Comaniciu et al., “Distribution Free Decomposition of Multivariate Data,” Pattern Analysis & Application, 2:22-30 (1999) discloses using a mean shift technique to decompose multivariate data. An iterative technique along with density estimate functions are used to reduce a large dataset to the few points that best describe the data.

Grimson et al., “Using Adaptive Tracking to Classify and Monitor Activities in a Site,” Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 22-31, (1998) discloses using camera coordinates of objects that pass through the fields of view of cameras, along with time, to find correspondences between the cameras. Once all the camera views are mapped onto one camera view, this mosaic camera view can be mapped onto a virtual overhead plane. Coordinates in the virtual overhead plane are used to track moving objects.

Horprasert et al., “A Robust Background Subtraction and Shadow Detection,” Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan (January 2000) discloses using chromaticity data separate from intensity data to do background subtraction. All colors are treated as being on a line in a three dimensional space. The difference between two colors is calculated as the distance between their color lines.

Stauffer et al., “Adaptive Background Mixture Models for Real-Time Tracking,” Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 246-252 (1999) discloses performing background subtraction with models that change over time. More specifically, each pixel is represented by multiple Gaussian distributions.

None of these documents describe a technique for monitoring multiple cameras with fewer personnel and/or at greater efficiency by electronically filtering and alerting personnel as to which cameras show unauthorized activity and, more importantly, relieving personnel from some monitoring authorized activity as is described below.

SUMMARY

The invention involves viewing a three dimensional space from numerous camera angles at different exposures. The invention uses intensity and chromaticity data at the different exposures to create a pixel model for each pixel. These pixel models form the basis for a background model. A current image is compared with the background model to find pixels that have changed from their pixel model. These novel pixels are processed using contiguous region detection and grouped into foreground figures. For each camera, features are extracted from each foreground figure. These features are then placed in a queue with features from other cameras and sent to a central processor.

The central processor maintains an object model for each foreground figure. The central processor also maintains a world model. The world model is based upon the background models and each of the object models. Once the central processor has processed the features from a particular foreground figure, the central processor compares the features with the object models in the world model. If the features match one of the pre-existing object models, the central processor updates the corresponding object model. If not, the central processor adds a new object model to the world model.

A graphical user interface displays the relative locations of foreground figures in the world model. The location and identification of the foreground figures is checked against a table of permissions so as to selectively generate an alert. The graphical user interface also provides for real-time viewing and manual identification of the foreground figures.

Camera video can be analyzed in a cellular strategy. If a figure leaves or is about to leave a cell, the preferred embodiment accounts for its approximate position and notifies adjacent cells of the expected arrival of the foreground figure. The system maintains local and global object identifications (ids) for each figure. If a new figure enters the area, an assertion is made that the global id and the local id are the same. If an object in a cell is determined to be the same object as an object from another cell, the two objects are connected (or associated together) and are given the same global id.

The invention can be advantageous in that the pixel models are able to account for overexposed and underexposed pixels, changes in lighting, repetitive motion, and other causes of false alarms.

The invention can be advantageous in that it is possible to record changes to the world model rather than pixel changes. In other words, a more sophisticated method of reasoning about the world allows for more concise storage of the fact that an event occurred. This allows the invention to require less storage space to store the similar information. Also, this allows the invention to use less bandwidth to transfer information to another point. Less storage space is required because in preferred embodiments, changes in the world model are stored, rather than storing changes to successive images in a video stream. Also, the selection of which images are stored can be more appropriate because it can be in terms of easier-to-express and understand descriptions of impermissible changes in a world model, rather than pixel changes in images. Further, less bandwidth is required because a world model is used, rather than successive images in a video stream. This affords a more succinct representation.

The invention can be advantageous in that it is able to generate fewer false alarms because the alarms are based on changes to the world model rather than pixel changes. More specifically, depending on implementation, a user is likely to experience fewer false positives and/or false negative alert conditions because the user can better establish criteria as to what should cause the user to be notified in terms of changes with respect to a world model, rather than pixel changes in an image stream. This is in contrast to systems that compress images both spatially and temporarily in the image domain (e.g., MPEG). This other systems might express an alarm condition in terms of a 2-D sub-image, which moves in a certain direction in part of an image. This is a contrast to the inventor's preferred implementation of combining a variable image into a world model and then storing, searching, generating alarms and transmitting image information in terms with respect to that world model.

Another advantage of a preferred embodiment is that is possible to track individual objects that are within the view of one or more cameras and then hand-off the object from one cellular group of cameras to a neighboring video cell.

Another advantage of a preferred embodiment is that it is possible to distinguish any object that violates a rule of security, safety, or otherwise by its location, velocity, acceleration, path, or any combination of present or historical data.

Another advantage of a preferred embodiment is that is possible to view a single graphical interface rather than a large number of monitors.

Another advantage of a preferred embodiment is that it is possible to manually or automatically view any object as it moves from one camera view to the next.

Another advantage of a preferred embodiment is to view a selected object from all or more than one simultaneously available camera view.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing, and other objects, features, and advantages of the invention will be more readily understood upon reading the following detailed description of exemplary embodiments in conjunction with the drawings in which:

<figref idrefs="DRAWINGS">FIG. 1</figref> is an illustration of a three dimensional area with video surveillance cameras arranged in a cellular format;

<figref idrefs="DRAWINGS">FIG. 2</figref> is a more detailed view of a foreground figure;

<figref idrefs="DRAWINGS">FIG. 3</figref> is an even more detailed view of the foreground figure;

<figref idrefs="DRAWINGS">FIG. 4</figref> is a high-level flow chart of a method for video surveillance in accordance with the invention;

<figref idrefs="DRAWINGS">FIG. 5</figref> is a more detailed view of the step of creating a background model;

<figref idrefs="DRAWINGS">FIG. 6</figref> is a more detailed view of the step of background subtraction;

<figref idrefs="DRAWINGS">FIG. 7</figref> is a even more detailed view of the step of background subtraction;

<figref idrefs="DRAWINGS">FIG. 8</figref> is another even more detailed view of the step of background subtraction;

<figref idrefs="DRAWINGS">FIG. 9</figref> is a more detailed view of one aspect of grouping novel pixels into foreground figures;

<figref idrefs="DRAWINGS">FIG. 10</figref> is a more detailed view of another aspect of grouping novel pixels into foreground figures;

<figref idrefs="DRAWINGS">FIG. 11</figref> is an illustration of a graphical user interface and a graphical representation of the three dimensional area; and

<figref idrefs="DRAWINGS">FIG. 12</figref> is a time-lapsed illustration of a three dimensional area with video surveillance cameras arranged in a cellular format.

DETAILED DESCRIPTION

In the following description, specific details are discussed in order to provide a better understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods are omitted so as not to obscure the description of the invention with unnecessary detail.

Referring now to the drawings, <figref idrefs="DRAWINGS">FIG. 1</figref> is an illustration of a three dimensional area with video surveillance cameras arranged in a cellular format. A three dimensional area <b>1</b> is divided into, for example, three adjacent video cells <b>90</b>, <b>91</b>, and <b>92</b>. Many more video cells would likely be employed in a practical implementation. The cells <b>90</b>, <b>91</b>, and <b>92</b> may or may not overlap depending on the specific implementation of a system. While an outdoor setting is pictured, the cells <b>90</b>, <b>91</b>, and <b>92</b> may correspond to different parts (hallways, rooms, open areas, etc.) of any building (home, office, museum, sports arena, public, military, or private facility, etc.).

A first camera station <b>10</b> has one or more cameras. These cameras may have a fixed view or be remotely controlled. In addition, they may have a fixed location or be mobile. In either case, the location and/or perspective of a particular camera is either known or calculated from sensor data and/or image analysis.

In <figref idrefs="DRAWINGS">FIG. 1</figref>, a first camera <b>10</b><i>a </i>is positioned so as to view a first cell <b>90</b>; a second camera <b>10</b><i>b </i>is positioned so as to view a second cell <b>91</b>; and a third camera <b>10</b><i>c </i>is positioned so as to view another cell (not shown), etc. Similarly, at camera station <b>11</b>, there is a first camera <b>11</b><i>a </i>for viewing the second cell <b>91</b> cell and a second camera <b>11</b><i>b </i>for viewing a third cell <b>92</b>. At camera station <b>12</b>, there are three cameras <b>12</b><i>a</i>-<i>c </i>for viewing the three cells <b>90</b>, <b>91</b>, and <b>92</b>, respectively.

As discussed in more detail below, different cameras (e.g., <b>10</b><i>a </i>and <b>12</b><i>a</i>) view different surfaces of the same volume (e.g., cell <b>90</b>) of the three dimensional space <b>1</b>. The output signals of these cameras are then processed to create pixel models that are used to produce a background model of the three dimensional space <b>1</b>. This background model is used to differentiate foreground figures from the background (i.e. what is normally there).

In <figref idrefs="DRAWINGS">FIG. 1</figref>, stationary or permanent objects such as the tree <b>40</b> and the building <b>44</b> become part of the background. While the tree <b>40</b> may include swaying branches and building <b>44</b> may include swinging doors or a flag blowing in the wind, these objects are treated as part of the background. Once the background model is complete, a process of background subtraction is performed to obtain pixels that have changed.

In <figref idrefs="DRAWINGS">FIG. 1</figref>, foreground <figref idrefs="DRAWINGS">FIGS. 30</figref>, <b>36</b>, and <b>38</b> are moving in directions a, b, and c, respectively. As these foreground figures move, they cause pixels to change. These novel pixels are then processed using contiguous region detection.

<figref idrefs="DRAWINGS">FIG. 2</figref> is a more detailed view of foreground <figref idrefs="DRAWINGS">FIG. 30</figref>. The foreground <figref idrefs="DRAWINGS">FIG. 30</figref> is viewed by two cameras <b>10</b><i>a </i>and <b>12</b><i>a</i>. <figref idrefs="DRAWINGS">FIG. 3</figref> is an even more detailed view of foreground <figref idrefs="DRAWINGS">FIG. 30</figref>. Once foreground <figref idrefs="DRAWINGS">FIG. 30</figref> is identified and processed, it is possible to calculate the intersection of vectors in real space so as to calculate the approximate location of <figref idrefs="DRAWINGS">FIG. 30</figref>. Those skilled in the art recognize that there are many factors involved in properly discerning foreground figures. For example, lighting changes can cause pixels to be either under or overexposed. When this happens, what was learned as a background may not work. In addition, leaves blowing in the wind, waves lapping on a beach, or clouds passing in front of the sun may create the false impression of a foreground object. Techniques discussed below in connection with <figref idrefs="DRAWINGS">FIGS. 4-8</figref> overcome these and other challenges involved in doing background subtraction.

<figref idrefs="DRAWINGS">FIG. 4</figref> is a high-level flow chart of a method for video surveillance in accordance with the invention. As described above, two or more cameras view different surfaces of the same volume of a three-dimensional space. Each camera stores chromaticity and intensity data for multiple images at multiple exposures. This function is performed, for example, by a local processor, local ASIC, or a centrally located server.

In step <b>100</b>, the chromaticity and intensity data is used to model the three-dimensional area and create a background model. More specifically, each camera analyzes the chromaticity and intensity date to create a pixel model for each pixel. These pixel models form the basis for the background model.

In step <b>200</b>, a current image is compared with the background model to find pixels that have changed from their pixel model. While a pixel may have changed, if it is consistent with its pixel model (i.e. it has not changed beyond a certain threshold), this pixel is included in the model and “turned off.” If on the other hand, the pixel is inconsistent with its pixel model, this pixel is treated as a novel pixel and “turned on.”

In step <b>300</b>, adjacent novel pixels are grouped into foreground figures as described in more detail below.

In step <b>400</b>, each camera extracts features from each foreground figure. These features are then placed in a queue with features from other cameras.

In step <b>500</b>, a central processor (or in some cases, the same processor) processes the queue of features. If multiple cameras are viewing the same foreground figure, the central processor should receive at least one feature from each camera. While it is possible to synchronize the cameras, it is not necessary. If, for example, each camera identifies each feature with a particular moment in time, the central processor can process the features accordingly. For example, the central processor can prioritize the image frames based upon time. If so, the central processor can collect all features from multiple images from multiple cameras, reorder the images, then process all the features from each image as a group. The fact that the features in the group are not from the same instant in time is accounted for as described below.

As the central processor processes features from the input cameras, the central processor maintains an object model for each foreground figure. The central processor also maintains a world model. The world model is based upon the background models and each of the object models.

Once the central processor has processed the features for a particular foreground figure, the central processor compares the features with the object models in the world model.

If it is determined that the features match one of the pre-existing object models, the central processor updates the corresponding object model in step <b>600</b>. For example, the central processor updates the position of the object model and/or adds additional details.

If on the other hand, the central processor determines that the features do not match any of the pre-existing object models, the central processor adds a new object model to the world model in step <b>700</b> based upon the currently known information.

Referring now to <figref idrefs="DRAWINGS">FIG. 5</figref>, the step of creating a background model <b>100</b> is described in more detail. As shown in step <b>99</b>, a camera records an input image at a current exposure. The number of pixels in the input image depends on the resolution of the camera.

In step <b>101</b>, the red, green, and blue intensity of each pixel is stored.

In step <b>102</b>, the red, green, and blue intensity of each pixel is normalized based upon the current exposure and gain settings.

In step <b>103</b>, color ratios are calculated for each pixel. The color ratios are the measured value of one color divided by the measured value of another color. According to an exemplary embodiment, the ratios of red-to-green and blue-to-green are calculated and these ratios are used to measure chromaticity. However, it will be evident to those skilled in the art that other ratios or other methods of measuring chromaticity can be used.

In step <b>104</b>, it is determined whether or not the system is in training mode. If so, the system begins to or continues to build a pixel model for each pixel. If not, the system proceeds to background subtraction. While <figref idrefs="DRAWINGS">FIG. 5</figref> shows the steps of building a pixel model <b>105</b> and background subtraction <b>200</b> as being mutually exclusive, it is possible to continuously update the model while performing background subtraction.

In step <b>107</b>, the current red, green, and blue intensity values for each pixel are compared to an underexposed threshold. The underexposed threshold is the point at which is either impossible or too computationally expensive to tell what the actual intensity is because there is too much noise relative to the measured “signal” of light intensity.

In step <b>108</b>, the current pixel values are compared to an overexposed threshold. The overexposed threshold is determined by the point at which exposure no longer increases linearly with a linear increase in light intensity (without gamma correction).

In step <b>110</b>, it is determined whether the current pixel values are normally (or correctly) exposed. A pixel is correctly exposed if it is not underexposed or overexposed.

In step <b>111</b>, data is saved in pixel model (i, j), and in steps <b>107</b>, <b>108</b> and <b>110</b>, the total number of times that a given pixel value for pixel (i, j) is overexposed, underexposed, or correctly exposed is counted. For example, if the red intensity value for pixel (i,j) is correctly exposed, the red intensity value is saved in pixel model (i, j) and the total number of times that the red value for pixel (i, j) is correctly exposed is increased by one. Similarly, if the green intensity value for pixel (i, j) is correctly exposed, the green intensity value is saved in the model and the total number of times that green is correctly exposed is also increased by one. If, however, the blue intensity is underexposed, the blue intensity value is not saved in the model and the total number of times that the blue value for pixel (i, j) is underexposed is increased by one. Because the red and green values for pixel (i, j) are correctly exposed, the red-to-green ratio is also saved in pixel model (i, j). However, because the blue value for pixel (i, j) is incorrectly exposed, the blue-to-green ratio is not saved in pixel model (i, j).

In step <b>120</b>, the average (and the standard deviation) of each pixel value stored in pixel model (i, j) is updated. For example, if red, green, and blue intensity values and red-to-green and blue-to-green ratios are stored in pixel model (i, j), the system calculates the average (and the standard deviation) of: <ul><li id="ul0001-0001" num="0000"><ul><li id="ul0002-0001" num="0069">(1) all the red intensity values in pixel model (i, j);</li><li id="ul0002-0002" num="0070">(2) all the blue intensity values in pixel model (i, j);</li><li id="ul0002-0003" num="0071">(3) all the green intensity values in pixel model (i, j);</li><li id="ul0002-0004" num="0072">(4) all the red-to-green ratios in pixel model (i, j); and</li><li id="ul0002-0005" num="0073">(5) all the blue-to-green ratios in pixel model (i, j).</li></ul></li></ul>

After calculating standard deviation and average in the previous paragraph, another part of step <b>120</b> is to adjust average and standard deviation if the data is determined to be near either edge of the range of values that are collectable by the image sensor. In other words, there needs to be an adjustment if the data is very close to the minimum value detectable or if the data is very close to the maximum value detectable without being overexposed. For this purpose, the following values are used: <ul><li id="ul0003-0001" num="0000"><ul><li id="ul0004-0001" num="0075">(1) Sigma.pixel is the standard deviation as measured for a given pixel;</li><li id="ul0004-0002" num="0076">(2) Sigma.camera is the “known” standard deviation of a normal pixel as a function of a given camera system (i.e., lens, imager);</li><li id="ul0004-0003" num="0077">(3) Nu.Max is the underexposed threshold normalized to the maximum exposure and gain at which the camera trains; and</li><li id="ul0004-0004" num="0078">(4) No.Min is the overexposed threshold normalized to the minimum exposure and gain at which the camera trains.</li></ul></li></ul>

A value is considered to be near one of the thresholds if: <ul><li id="ul0005-0001" num="0000"><ul><li id="ul0006-0001" num="0080">(a) N_avg+3.5sigma.pixel&lt;Nu.Max+3.5sigma.camera, or</li><li id="ul0006-0002" num="0081">(b) N_avg−3.5sigma.pixel&gt;No.Min−3.5sigma.camera, in one example. <br /> If (a) is true, then N_avgis changed to be equal to N_avg+3.5sigma.pixel−3.5sigma.camera, and sigma.pixel is set to sigma.camera. However, if (b) is true, then N_avgis changed to be equal to N_avg−3.5sigma.pixel+3.5sigma.camera, and sigma.pixel is set to sigma.camera. Basically, the idea is that if the collected average is very close to the minimum or maximum normalized values that the camera can see, then correct data was most likely not collected, so the average and standard deviation are slightly adjusted for better results. </li></ul></li></ul>

Those skilled in the art will appreciate that repetitive motion (such as leaves blowing in the wind or waves lapping on a beach) will lead to a larger standard deviation. While <figref idrefs="DRAWINGS">FIG. 5</figref> illustrates updating the average (and the standard deviation) of each pixel value each time the training process is performed, it may be preferably to perform this calculation at the end of the training process. It is also possible to have multiple models per pixel or to have multiple pixels represented by the same model.

In step <b>130</b>, the current exposure is maintained; the current exposure is incrementally increased (or decreased); or the best exposure is selected depending on the training process. For example, in an exemplary embodiment, the training process is performed one hundred twenty-five times. The first twenty-five images are recorded at an exposure time of 20 milliseconds. The next fifteen images are recorded at an exposure time of 25 ms. After which groups of fifteen images are recorded at exposure times of 30 ms, 40 ms, and 45 ms. Finally, twenty-five images are recorded at an exposure time of 50 ms. Obviously, the twenty-five images recorded at an exposure time of 20 ms are more likely to be underexposed. And the twenty-five images recorded at 50 ms are more likely to be overexposed. However, those skilled in the art will appreciate it is advantageous to record that more images at the lowest and highest exposures. Some pixels will only be normally exposed at either the lowest or highest exposure settings, and therefore, they will be on the “border” between normally exposed and incorrectly exposed. More images should be taken to get enough samples of a given pixel to assure that a normally exposed sample is acquired for that pixel.

As discussed above, the total number of underexposed values, overexposed values, and correctly exposed values is counted. Therefore, for a given intensity or ratio, the maximum number of correctly exposed values is one hundred twenty-five in this example. In some cases, there may not be any correctly exposed values. At the end of the training process, the best exposure time for the image (i.e. the collective best exposure time for all of the pixels) is continually sought through an auto-exposure algorithm.

Referring now to <figref idrefs="DRAWINGS">FIG. 6</figref>, the step of background subtraction <b>200</b> is described in more detail. As explained above, at the end of the training process, the best exposure time is selected. Therefore, in step <b>201</b>, the input to the background subtraction process is a current image at the best exposure. The number of pixels in the current image depends on the resolution of the camera.

As will be demonstrated below, the step of background subtraction <b>200</b> electronically filters the current image to leave only novel pixels. In a less complex system, the step of background subtraction may be limited to, for example, step <b>210</b> or a variation thereof. There can be additional safeguards to prevent false alarms.

In step <b>210</b>, it is determined whether the overall intensity of pixel (i, j) is consistent with pixel model (i, j). The overall intensity of pixel (i, j) may, for example, be the sum of the red, blue, and green intensities of pixel (i, j). As described above, in relation to step <b>126</b>, the average of each pixel value stored in pixel model (i, j) is calculated. If, for example, the system stored the average red intensity value, the average blue intensity value, and the average green intensity value, the expected overall intensity would be the sum of these three averages. If, for example, the overall intensity of pixel (i, j) is greater than half the expected intensity and less the twice the expected intensity, pixel (i, j) is consistent with the model. If the overall intensity of pixel (i, j) is less than half the expected intensity or greater than twice the expected intensity, pixel (i, j) is inconsistent with the model. Of course, it is possible to make the system more (or less) sensitive by using a narrower (or wider) window of acceptable values.

If the overall intensity of pixel (i, j) is within an acceptable window of values, the system proceeds to step <b>220</b> to check chromaticity. If not, the system proceeds to step <b>240</b>. In a less complex system, pixel (i, j) may be treated as a novel pixel. Additional steps can be performed to determine to greater degree if pixel (i, j) is an acceptable pixel.

In step <b>220</b>, it is determined whether the chromaticity of pixel (i, j) is consistent with pixel model (i, j) as described in more detail below. If so, the system can proceed to step <b>230</b>. If not, the system should proceed to step <b>240</b>.

In step <b>230</b>, it has been determined that pixel (i, j) is consistent with both the overall intensity part of the pixel model and the chromaticity part of the pixel model. Therefore, pixel (i, j) can be ignored and treated as part of the model. Once it has been determined that pixel (i, j) is part of the model, the next pixel in the current image can be processed starting from step <b>201</b> until there are no pixels left.

If on the other hand, it is determined that pixel (i, j) is inconsistent with the model, the pixel can be treated as a novel pixel or additional processing can be performed as described below.

In step <b>240</b>, the red intensity value is individually analyzed. If the red intensity value is consistent with the pixel model, the system proceeds to step <b>250</b>. If not, pixel (i, j) is treated as novel and the system can process the next pixel until there are no pixels left.

In step <b>250</b>, the green intensity value is individually analyzed. If the green intensity value is consistent with the pixel model, the system proceeds to step <b>260</b>. If not, pixel (i, j) is treated as novel and the next pixel is processed until there are no pixels left.

In step <b>260</b>, the blue intensity value is individually analyzed. If the blue intensity value is consistent with the pixel model, the system proceeds to step <b>230</b>. If not, pixel (i, j) is treated as novel and the next pixel is processed until there are no pixels left.

Once it is determined that there are no more pixels left, the novel pixels identified in step <b>270</b> are furthered processed in step <b>300</b>.

Referring now to <figref idrefs="DRAWINGS">FIG. 7</figref>, step <b>220</b> is described in more detail. As described above, the chromaticity of pixel (i, j) may, for example, be measured using the red-to-green and blue-to-green ratios. If this is the case, then there should be stored in pixel model (i, j) an average (and standard deviation) for these two ratios.

In step <b>221</b>, the root sum square distance is calculated. That is, the red-to-green ratio of pixel (i, j) is compared with the average red-to-green ratio stored in pixel model (i, j) and the difference is measured in standard deviations. Next, the blue-to-green ratio of pixel (i, j) is compared with the average blue-to-green ratio and the difference is also measured in standard deviations. Finally, the square root of the first difference squared plus the second difference squared is calculated.

In step <b>222</b>, if the root sum square mean is less than 3.5 sigma, then pixel (i, j) is consistent with the model and the system can proceed to step <b>230</b>. If not, the system proceeds to step <b>223</b>. As explained above, in relation to step <b>210</b>, it is possible to make the system more (or less) sensitive by using a narrower or wider window of acceptable values.

In step <b>223</b>, the following table (Table 1) is performed for both ratios.

<tables id="TABLE-US-00001" num="00001"><table frame="none" colsep="0" rowsep="0"><tgroup align="left" colsep="0" rowsep="0" cols="4"><colspec colname="1" colwidth="49pt" align="center" /><colspec colname="2" colwidth="56pt" align="center" /><colspec colname="3" colwidth="56pt" align="center" /><colspec colname="4" colwidth="56pt" align="center" /><thead><row><entry namest="1" nameend="4" rowsep="1">TABLE 1</entry></row><row><entry namest="1" nameend="4" align="center" rowsep="1" /></row><row><entry /><entry>Numerator</entry><entry>Numerator</entry><entry>Numerator</entry></row><row><entry /><entry>Out of Range High</entry><entry>In Range</entry><entry>Out of Range Low</entry></row><row><entry namest="1" nameend="4" align="center" rowsep="1" /></row></thead><tbody valign="top"><row><entry>Denominator</entry><entry>Ratio Undefined</entry><entry>Ratio </entry><entry>Ratio </entry></row><row><entry>Out of </entry><entry>(Both Low)</entry><entry>Approaches Zero</entry><entry>Approaches Zero</entry></row><row><entry>Range High</entry><entry /><entry /><entry /></row><row><entry>Denominator</entry><entry>Ratio Approaches</entry><entry>Ratio Normal</entry><entry>Ratio </entry></row><row><entry>In Range</entry><entry>Infinity</entry><entry /><entry>Approaches Zero</entry></row><row><entry>Denominator</entry><entry>Ratio Approaches</entry><entry>Ratio Approaches </entry><entry>Ratio Undefined</entry></row><row><entry>Out of </entry><entry>Infinity</entry><entry>Infinity</entry><entry>(Both High)</entry></row><row><entry>Range Low</entry></row><row><entry namest="1" nameend="4" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

Using the red-to-green ratio as an example, if the red intensity value is out of range high and the green intensity value is in range, then the numerator is out of range high and the denominator is in range. Therefore, using the table above the ratio approaches infinity.

In step <b>224</b>, the following (Table 2) is utilized with the output of Table 1 for both ratios.

<tables id="TABLE-US-00002" num="00002"><table frame="none" colsep="0" rowsep="0" pgwide="1"><tgroup align="left" colsep="0" rowsep="0" cols="6"><colspec colname="1" colwidth="49pt" align="center" /><colspec colname="2" colwidth="56pt" align="center" /><colspec colname="3" colwidth="56pt" align="center" /><colspec colname="4" colwidth="56pt" align="center" /><colspec colname="5" colwidth="42pt" align="center" /><colspec colname="6" colwidth="42pt" align="center" /><thead><row><entry namest="1" nameend="6" rowsep="1">TABLE 2</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row><row><entry /><entry /><entry /><entry /><entry>R/G</entry><entry>R/G</entry></row><row><entry /><entry>R/G Approaches</entry><entry /><entry>R/G Approaches</entry><entry>Undefined</entry><entry>Undefined</entry></row><row><entry /><entry>Infinity</entry><entry>R/G Normal</entry><entry>Zero</entry><entry>(Both High)</entry><entry>(Both Low)</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row></thead><tbody valign="top"><row><entry>B/G</entry><entry>R/G &lt;= R/G avg.</entry><entry>R/G within 3.5σ of</entry><entry>R/G &gt;= R/G avg.</entry><entry>*</entry><entry>*</entry></row><row><entry>Approaches</entry><entry>and</entry><entry>R/G avg. and</entry><entry>and B/G &lt;= B/G</entry><entry /><entry /></row><row><entry>Infinity</entry><entry>B/G &lt;= B/G avg.</entry><entry>B/G &lt;= B/G avg.</entry><entry>avg.</entry><entry /><entry /></row><row><entry>B/G Normal</entry><entry>R/G &lt;= R/G avg.</entry><entry>Null</entry><entry>R/G &gt;= R/G avg.</entry><entry>*</entry><entry>*</entry></row><row><entry /><entry>and B/G within</entry><entry /><entry>and B/G within</entry><entry /><entry /></row><row><entry /><entry>3.5σ of B/G avg.</entry><entry /><entry>3.5σ of B/G avg.</entry><entry /><entry /></row><row><entry>B/G</entry><entry>R/G &lt;= R/G avg.</entry><entry>R/G within 3.5σ of</entry><entry>R/G &gt;= R/G avg.</entry><entry>*</entry><entry>*</entry></row><row><entry>Approaches</entry><entry>and B/G &gt;= B/G</entry><entry>R/G avg. and</entry><entry>and B/G &gt;= B/G</entry><entry /><entry /></row><row><entry>Zero</entry><entry>avg.</entry><entry>B/G &gt;= B/G avg.</entry><entry>avg.</entry><entry /><entry /></row><row><entry>B/G Undefined</entry><entry>*</entry><entry>*</entry><entry>*</entry><entry>*</entry><entry>*</entry></row><row><entry>(Both High)</entry><entry /><entry /><entry /><entry /><entry /></row><row><entry>B/G Undefined</entry><entry>*</entry><entry>*</entry><entry>*</entry><entry>*</entry><entry>*</entry></row><row><entry>(Both Low)</entry></row><row><entry namest="1" nameend="6" align="center" rowsep="1" /></row></tbody></tgroup></table></tables>

In the table above, there are eight Boolean statements, one null, and sixteen asterisks. In the event that there is a Boolean statement, if the Boolean statement is true, the pixel is consistent with the model and the system can proceed to step <b>230</b>. Otherwise, the system should proceed to steps <b>225</b>-<b>229</b>.

In step <b>225</b>, if the red-to-green ratio of the model for pixel (i, j) is undefined, pixel (i, j) is consistent with pixel model (i, j) if the blue-to-green ratio is within 3.5 sigma of the average blue-to-green ratio. If so, the system can proceed to step <b>230</b>. If not, the system should proceed to step <b>226</b>.

In step <b>226</b>, if the blue-to-green ratio of the model for pixel (i, j) is undefined, pixel (i, j) is consistent with pixel model (i, j) if the red-to-green ratio is within 3.5 sigma of the average red-to-green ratio. If so, the system can proceed to step <b>230</b>. If not, the system should proceed to step <b>227</b>.

In step <b>227</b>, if the red and blue intensity values of pixel (i, j) are both incorrectly exposed in the way that they are expected to be incorrectly exposed, then pixel (i, j) is consistent with the model. In other words, if red is currently overexposed and blue is currently overexposed, and in the majority of the pixels during the training process, red was overexposed and blue was overexposed, then pixel (i, j) is consistent with the model. If so, the system can proceed to step <b>230</b>. If not, the system should proceed to step <b>228</b>.

In step <b>228</b>, if the green intensity value of pixel (i, j) is incorrectly exposed in the way that it is expected to be incorrectly exposed, the pixel (i, j) is consistent with the model. If so, the system can proceed to step <b>230</b>. If not, the system should proceed to step <b>229</b>.

In step <b>229</b>, if either ratio is undefined (low or high) and the red, green, and blue intensity values of pixel model (i, j) are all in there respective intensity models (as explained below), then pixel (i, j) is consistent with pixel model (i, j). If so, the system can proceed to step <b>230</b>. If not, the pixel can be treated as a novel pixel or additional processing can be performed as described above.

Referring now to <figref idrefs="DRAWINGS">FIG. 8</figref>, the step of analyzing the red intensity value <b>240</b> is described in more detail. In step <b>241</b>, if the red intensity value of pixel (i, j) is within 3.5 standard deviations of the average red intensity value stored in pixel model (i, j), then the red intensity value is consistent with the model. If so, the system can proceed to step <b>250</b>. If not, the system should proceed to step <b>244</b>.

In steps <b>244</b> and <b>245</b>, there are six inputs: <ul><li id="ul0007-0001" num="0000"><ul><li id="ul0008-0001" num="0112">(1) the actual measured (i.e. not normalized) red intensity value of pixel (i, j) denoted “C”;</li><li id="ul0008-0002" num="0113">(2) the actual intensity value for the underexposed threshold denoted “C_U”;</li><li id="ul0008-0003" num="0114">(3) the actual intensity value for the overexposed threshold denoted “C_O”;</li><li id="ul0008-0004" num="0115">(4) the number of values that were underexposed during the training period denoted “UN”;</li><li id="ul0008-0005" num="0116">(5) the number of values that were overexposed during the training period denoted “OV”; and</li><li id="ul0008-0006" num="0117">(6) the number of values that were correctly exposed during the training period denoted “CE”</li></ul></li></ul>

In step <b>244</b>, which examines the case where the pixel is never correctly exposed, if CE is equal to zero, UN&gt;OV, and C is less than or equal to C_U, then pixel (i, j) is consistent with pixel model (i, j). If so, the system can proceed to step <b>250</b>. If not, the system should proceed to step <b>245</b>.

In step <b>245</b>, which also examines the case where the pixel is never correctly exposed, if CE is equal to zero, UN&lt;OV, and C is greater than or equal to C_O, then pixel (i, j) is consistent with pixel model (i, j). If so, the system can proceed to step <b>250</b>. If not, the system should proceed to step <b>246</b>.

In steps <b>246</b>-<b>248</b>, there are six inputs: <ul><li id="ul0009-0001" num="0000"><ul><li id="ul0010-0001" num="0121">(1) the actual measured (i.e. not normalized) red intensity value of pixel (i, j) denoted “C”;</li><li id="ul0010-0002" num="0122">(2) the average normalized red intensity value of pixel model (i, j) denoted “N_avg”;</li><li id="ul0010-0003" num="0123">(3) the current exposure;</li><li id="ul0010-0004" num="0124">(4) the current gain;</li><li id="ul0010-0005" num="0125">(5) the actual intensity value for the underexposed threshold denoted “C_U”; and</li><li id="ul0010-0006" num="0126">(6) the actual intensity value for the overexposed threshold denoted “C_O”;</li></ul></li></ul>

In step <b>246</b>, the predicted non-normalized value denoted “C_predict” is calculated using N_avg, the current exposure, and the current gain.

In step <b>247</b>, which examines the low end, if C is less than or equal to C_uand C_predictis less than or equal to C_uthen pixel (i, j) is consistent with pixel model (i, j). If so, the system can proceed to step <b>250</b>. If not, the system should proceed to step <b>248</b>.

In step <b>248</b>, which examines the high end, if C is greater than or equal to Co and C_predictis greater than or equal to Co, then pixel (i, j) is consistent with pixel model (i, j). If so, the system can proceed to step <b>250</b>. If not the system should proceed to step <b>270</b>.

As explained above in connection with <figref idrefs="DRAWINGS">FIG. 6</figref>, if the red intensity value is individually analyzed in step <b>240</b> and determined to be consistent with the model, then the green intensity value is individually analyzed in step <b>250</b> and the blue intensity value is individually analyzed in step <b>260</b>. Therefore, the process described above in connection with <figref idrefs="DRAWINGS">FIG. 8</figref> should be repeated for the green intensity value in step <b>250</b> and the blue intensity value in step <b>260</b>.

Referring now to <figref idrefs="DRAWINGS">FIG. 9</figref>, the step of getting foreground <figref idrefs="DRAWINGS">FIG. 30</figref> is described in more detail. The novel pixels are grouped into foreground figures using a process called contiguous region detection.

In step <b>350</b>, novel pixel (i, j) is selected.

In step <b>360</b>, pixel (i, j) is labeled as being in an existing region, no region, or a new region.

In step <b>370</b>, if two different regions are now made to be adjacent by the addition of pixel (i, j), one of the two regions is joined with the other.

In step <b>380</b>, as pixel (i, j) is added to the region, the average of all the i values and the average of all the j values for all the pixels in the region is updated. The average (i, j) of the region represents an approximate location of the region.

As shown by step <b>390</b>, the next pixel is processed until there are no pixels left.

According to a further embodiment, the novel pixels are grouped into foreground figures using a process called color contiguous region detection. In order to perform color contiguous region detection, it is advantageous to generate a set of color modes.

Referring now to <figref idrefs="DRAWINGS">FIG. 10</figref>, the process of generating a set of color modes is described in more detail. In step <b>310</b>, a list of colors is sorted by frequency of appearance (how often a color appears in an image).

In step <b>320</b>, the most frequent color is picked as the first mode.

In step <b>330</b>, all pixels within a statistically significant color range from the first mode are selected to form group N.

As shown by step <b>340</b>, steps <b>320</b> and <b>330</b> are repeated until there are no colors (or only infrequently occurring colors) left.

In the case of color contiguous region detection, existing regions are only joined into one region if they are of the same color mode. If two adjacent regions are different colors, they are connected as adjacent regions, rather than joined.

Referring back to <figref idrefs="DRAWINGS">FIGS. 2 and 3</figref>, the novel pixels that make up foreground <figref idrefs="DRAWINGS">FIG. 30</figref> are either joined or connected. As regions <b>30</b><i>a</i>-<i>h </i>are joined or connected, the average (i, j) approaches the location (or pixel) indicated by the x.

As multiple cameras view the same foreground object from different angles, images can be and usually are captured at irregular intervals. As demonstrated below, it is possible to handle asynchronous, possibly out of order images.

Once the novel pixels that make up foreground <figref idrefs="DRAWINGS">FIG. 30</figref> are either joined or connected, information such as the source camera, the time of the image, and the approximate location of the foreground figure can be packaged and placed onto a special purpose queue.

It may be advantageous to use a priority queue where priority is based on the time of the image. In order for the queue to release an image for processing, there should be a time difference between the time of the image and the current time. This time difference should account for the speed of the system. That is, the time difference should account for slower cameras, processors, or links such that an image is available from each camera before the images are released. Once images start coming out of the queue, features that have already been extracted from each image can be processed as a group of features from the same instant in time and compared to the world model.

Once the server has an average (i, j) for a given foreground figure from a first camera and an average (i, j) for the same foreground figure from a second camera, it is possible to calculate a new position estimate for the foreground figure.

The foreground figure and its known features are then compared with each of the object models in the world model. These features include, for example, the object location, object direction, object velocity, object acceleration, object size, and position estimates. If it is determined that the foreground feature and its known features match one of the object models in the world model, then the corresponding object model is updated accordingly. If on the other hand, it is determined that the foreground figure does not match any of the existing object models, a new object model is added to the world model.

It is also possible to process asynchronous, possibly out of order images without using a priority queue. Rather than delaying processing and using the delay to reorder data, it is possible to process the data as it is received. In the event that data is received out of order or the data given to the world model is older than the current state of the world model, additional processing should be performed. The world model should be reverted to the youngest state older than the data that is currently being processed. Then, the world model is updated with the data currently being processed. Then, the world model is updated in order with all the data that has already been processed. In essence, the system reverts to a previous state, makes changes, then reapplies all the changes that have been made already with the changes that have been made.

One aspect of dealing with tracking objects is the notion of a position estimate. This is a volume used to represent the estimated current position at a time n. At a later time, usually some number of milliseconds, there is a new position estimate that incorporates object velocity, acceleration, and the previous position estimate to estimate a new volume of space where that object could possibly be at the current time. This position estimate is compared in step <b>500</b> of <figref idrefs="DRAWINGS">FIG. 4</figref> to new position estimates. When updating an object model, of which a position estimate is one feature, a new position estimate is calculated as the intersection of the current position estimate with the position estimate that is extracted from the current image. This particular method has the distinct advantage of being able to work with a system where images are collected asynchronously or synchronously and at regular or irregular intervals.

<figref idrefs="DRAWINGS">FIG. 11</figref> is an illustration of a graphical user interface and a graphical representation of the three dimensional area. The three dimensional area <b>801</b> is graphically represented on video interface <b>80</b>. Video interface <b>80</b> allows an observer to easily survey the net effect of hundreds or thousands of cameras. The video interface <b>80</b> displays relevant reference markers such as the location of camera stations <b>810</b>, <b>811</b>, and <b>812</b>; building <b>844</b>; and country border <b>845</b>. In an office setting, the video interface may, for example, display the floor layout with entrances and exits clearly marked.

Foreground <figref idrefs="DRAWINGS">FIGS. 830</figref>, <b>836</b>, and <b>838</b> are graphically displayed so as to indicate their relative position. In the example, foreground <figref idrefs="DRAWINGS">FIGS. 830</figref>, <b>836</b>, and <b>838</b> are an unidentified individual; an identified, authorized individual; and an automobile, respectively. A different icon, color or ID could be used for each.

If, for example, a system user wanted to manually view unidentified individual <b>830</b>, the user may click on camera station <b>810</b> to view individual <b>830</b> from camera <b>810</b><i>a</i>. Or alternatively, the user may click on individual <b>830</b> to view individual <b>830</b> from all relevant camera angles (i.e. cameras <b>810</b><i>a </i>and <b>812</b><i>a</i>).

The system checks the location and identification of the foreground figure against a table of permissions and generates alarms based upon the location of the foreground object and the table of permissions. For example, as individual <b>30</b> approaches country border <b>45</b>, a first alarm is generated. The icon <b>830</b> may blink, change color, become increasingly larger, or have some other programmed or scripted response. Audible signals or other alarm mechanisms may also be used. Also, images and/or model data may be transmitted to another location and/or saved based on alarms. If individual <b>30</b> actually crosses country border <b>45</b>, additional alarms are triggered and all relevant camera angles are automatically displayed. On the other hand, as individual <b>36</b> approaches country border <b>45</b>, no alarms are sounded because individual <b>36</b> is a specific identified, authorized individual.

In the case of automobile <b>38</b>, alarms may or may not be triggered based upon the relative speed of automobile <b>38</b>, the relative location of individual <b>36</b>, the time of day, recognition of the automobile by the computer, security personnel, or electronic or other remotely sensed tag, etc. If, for example, automobile <b>38</b> is authorized to cross the border, but unidentified individual <b>30</b> enters automobile <b>38</b> after automobile <b>38</b> crosses the border, automobile <b>38</b> is demoted from authorized to unauthorized. If, for example, unidentified individuals exit automobile <b>38</b> without entering building <b>44</b>, a different alarm might be triggered.

<figref idrefs="DRAWINGS">FIG. 12</figref> is a time-lapsed illustration of a three dimensional area with video surveillance cameras arranged in a cellular format. As explained in connection with <figref idrefs="DRAWINGS">FIG. 1</figref>, each cell <b>10</b>-<b>12</b> usually contains two or three cameras, although any number of cameras may be supported per video cell <b>10</b>-<b>12</b>.

The system works by having each cell <b>10</b>-<b>12</b> maintain a local object identification (id), while there is an overall global object identification (id) that is also assigned to each object. The purpose of this is to make it easy for each cell <b>10</b>-<b>12</b> to operate independently while maintaining a unified global identification system for client use. Each cell <b>10</b>-<b>12</b> tracks all the objects in its field of view independently, even where fields of view between cells overlap. In the latter case, the object is tracked in both cells contemporaneously.

Each cell <b>10</b>-<b>12</b> queries a server <b>2</b> to find out which cells are nearby. Alternatively, this information is provided at system startup time via a file. Cells <b>10</b>-<b>12</b> communicate with nearby cells by broadcasting their current state. This may be done in multiple ways. For example, each cell <b>10</b>-<b>12</b> opens a socket-based-client-server connection with nearby cells. When information is needed, cells <b>10</b>-<b>12</b> poll the nearby cells to obtain the needed information. Or alternatively, there is a “message queue” where cells <b>10</b>-<b>12</b> publish information on a “topic” and cells <b>10</b>-<b>12</b> subscribe to different “topics” as needed.

The system maintains local and global object ids. There is a data structure that contains associations between local and global ids. This data structure allows two primary operations: assert and connect. The first operation, assert, is usually performed when a new object enters a cell from outside the coverage of any cell. According to the operation assert, the new object is assigned a global id and a local id that are the same. In other words, an assertion is made that the global id and the local id are the same.

According to the operation connect, two objects from different cells are given the same global id. In other words, two objects from different cells are connected. The two objects are connected when the features from one object in a cell is determined to have enough similar features to and is within the position estimate of an object from another cell. The system is designed to check to make sure it does not make contradictory assertions or connections.

According to a particular embodiment, each exit/entry point of the surveillance area is defined as either a closed area entry point or an open area entry point. A closed area entry point occurs when there is only one exit from a closed area. An example of a closed area entry point is a closet door. When an object enters a closet, the object is assumed to remain in the closet until the object reemerges. When an object exits the closet, the object is matched (i.e. connected) with an object that previously entered the closet.

An example of an open area entry point is any entry point that does not follow the properties of a closed area entry point. An example of a closed area entry point is the front lobby of a office building. When an employee walks through the front lobby to go to lunch, the employee may or may not return through the front lobby. Therefore, when an object enters the front lobby, the object is checked against objects that previously exited the front lobby. In some cases, the objected is connected with a known object. In other cases, the objected is treated as a new object.

According to another embodiment, the system builds a cyclical graph of all the areas under surveillance. The system notes “world exit/entry” points on the graph by adding an “out-of-world” node. The system connects all nodes with world exits to the “out-of-world” node. The “out-of-world” node represents all the areas outside of the area of surveillance that are not bound by any constraints.

When building the graph, nodes must be added for areas that are outside of the area of surveillance, but have constraints. For example, there may be a section of a hallway that has two entry/exit points. This “out-of-coverage” area is represented as a node in the graph with edges connecting it to the two areas to which it is adjacent.

This graph allows for a great deal of reasoning about who is where, and it simplifies the tracking process by limiting the number possibilities for object mislabeling. Essentially, as an object moves out of view, it is placed not on a simple list of untracked objects, but on a list of objects in a certain “out of view” area.

When an object comes back into view, it is checked against the graph. Specifically, each area in the world has a corresponding graph node associated with it. When an object comes into an area like this, in order to maintain a cohesive label on an object as it passes through the area, the “out-of-view” list is checked for that specific section of the graph.

Using <figref idrefs="DRAWINGS">FIG. 12</figref> as an example, cells <b>10</b>-<b>12</b> are treated as open area entry points with the exception that building <b>44</b> is treated as a closed area entry point. When object <b>30</b> enters cell <b>90</b>, object <b>30</b> is assigned a local and a global object id. Because cell <b>90</b> is an open area entry point, object <b>30</b> is checked against a list of known objects. Because object <b>30</b> is a new object, object <b>30</b> is assigned a new global id and a new local id, where the global id and the local id are the same.

Similarly when object <b>38</b> enters cell <b>92</b>, object <b>32</b> is checked against a list of known objects. Because object <b>38</b> is a new object, object <b>38</b> is assigned a new global id and a new local id, where the global id and the local id are the same. However, when object <b>36</b> emerges from closed area entry point <b>44</b>, object <b>36</b> is matched with a known object and reassigned its old global id and old local id.

As object <b>30</b> moves within cell <b>90</b>, its position and features are updated. As object <b>30</b>′ leaves cell <b>90</b>, the server <b>2</b> accounts for the approximate position of object <b>30</b>′ and notifies adjacent cells <b>91</b> and <b>92</b> of the expected arrival of object <b>30</b>′. Similarly, as object <b>38</b>′ moves within cell <b>92</b>, its position and features are updated. As object <b>38</b>′ leaves cell <b>92</b>, the server <b>2</b> accounts for the approximate position of object <b>38</b>′ and notifies adjacent cells <b>90</b> and <b>92</b> of the expected arrival of object <b>38</b>′. However, when object <b>36</b>′enters closed area <b>44</b>, there is no need to notify adjacent cells <b>90</b> and <b>92</b>. Object <b>36</b>′ is assumed to remain in closed area <b>44</b> until object <b>36</b>′ reemerges.

When object <b>30</b>″ enters cell <b>92</b>, it is treated as a new foreground object and assigned a new local and global id. Once features are extracted from object <b>30</b>″ and it is determined that object <b>30</b>″ is the same object as object <b>30</b>, the two objects are connected and given the same global id. Similarly, when object <b>38</b>″ enters cell <b>92</b>, it is treated as a new foreground object and assigned a new local and global id. Once features are extracted from object <b>38</b>″ and it is determined that object <b>38</b>″ is the same object as object <b>38</b>, the two objects are connected and give the same global id. If object <b>30</b>″ enters object <b>38</b>″, object <b>38</b>″ may be treated as a closed area entry point and objects <b>30</b>″ and <b>38</b>″ may be associated and tracked and maintained as a joint object. However, should object <b>38</b>″ pass out of view of camera stations <b>10</b> and <b>11</b>, objects <b>30</b>″ and <b>38</b>″ should be disassociated to account for the possibility that object <b>30</b>″ exited object <b>38</b>″ while out of view of the camera stations.

While the foregoing description makes reference to particular illustrative embodiments, these examples should not be construed as limitations. It will be evident to those skilled in the art that the disclosed invention can be used in other embodiments. Thus, the present invention is not limited to the disclosed embodiments, but is to be accorded the widest scope consistent with the claims below.